During the next 10, 15, 20 years, society is going to look toward robotics for solutions to some of its big challenges: From improving the self-sufficiency and dignity of aging, to taking care of the next oil spill, passing through enabling cost-effective autonomous manufacturing, or actively supporting space and planetary exploration.

The ability to perform purposeful and reliable physical interaction is central to all these challenges. Unfortunately, our models of the mechanics of manipulation are usually based on simplifications and assumptions, which often fall short when applied to real problems and real scenarios. Enabling autonomous robotic manipulation in the real world requires to close the gap between models and reality, either by constructing more realistic models, or by quantifying the risk of failure of the inaccurate ones.

In the first part of the talk I will show recent work in the Manipulation Lab on constructing data-driven probabilistic models for manipulation and haptic perception that enable deriving manipulation plans with accurate predictions of the probability of success. These models are constructed with extensive offline experiments. I will illustrate the process with the example tasks of placing an object, dropping it in a cavity, or inserting it in a hole.

In the second part of the presentation I will talk about the most relevant aspects of the simple gripper used in the experiments, with an informed design that allows us to explore manipulation principles. In particular I will discuss our exploration of the problem of designing the shape of the fingers to satisfy some mechanical functionality. I will illustrate the technique with the example of designing the finger to improve the stability of a grasp.